Develop and optimize machine learning models for deployment on resource-constrained devices (edge computing, smartphones, and embedded systems)
Convert, compress, and quantize models (TensorFlow Lite, ExecuTorch, ONNX Runtime) to ensure low latency and efficient memory usage
Extract, transform, and analyze data from mobile application logs and propose new data sources when necessary
Implement MLOps pipelines for versioning, validation, and continuous deployment of embedded models
Collaborate with mobile development and framework teams to integrate predictive models into production
Monitor data and concept drift in already-deployed models, proposing retraining and updates

Proven experience (2 to 4 years) in data science with a focus on applied machine learning
Solid programming fundamentals (data structures, algorithms, Git version control, and clean code practices)
Proficiency in Python and libraries such as NumPy, Pandas, Scikit-learn, and TensorFlow/PyTorch
Experience with at least one embedded model format: ONNX, TensorFlow Lite, ExecuTorch
Good understanding of embedded systems and hardware constraints (memory, CPU, battery)
Advanced technical English (reading documentation, writing reports, and communicating with global teams)
Advanced English for reading research papers and collaborating with international teams
Basic mobile programming knowledge (Android/Kotlin) to support integration (plus)
Practical knowledge in model optimization for the edge (pruning, quantization, knowledge distillation) (plus)
Experience with MLOps frameworks (Kubeflow, MLflow, DVC) (plus)
Knowledge of signal processing (audio, accelerometer, gyroscope) for models on mobile devices (plus)
Familiarity with deploying to heterogeneous environments (ARM, mobile GPU, DSP) (plus)

40-hour workweek under CLT employment, flexible hours with a hybrid setup (4 days in the office and 1 day remote)
Gympass (WellHub), workplace stretching sessions, quick massage, and psychological support
Medical and dental plans for you and your family
Childcare assistance for our little SiDiers, 120-day maternity leave and extended paternity leave
Company contributions to private pension
Incentive program for continuing education and specialization, language fluency support, and weekly speaker series on global trend topics
Flexible Meal and Food Vouchers
Transportation allowance/subsidy to SiDi and parking for those working onsite
Annual performance bonus and rewards for SiDiers who achieve outstanding results
Committees focused on Well-being, Diversity, Mental Health, Social Impact, Sustainability, and Women's Inclusion in Technology
Relaxed, collaborative environments with communal spaces, a decompression room, a kitchenette, and coffee machines
Dozens of partner discounts and benefits

Mid-level Data Engineer

Key skills